April 2023 arXiv papers — page 120

Hongrun Zhang, Liam Burrows, Yanda Meng, Declan Sculthorpe

Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high

Alberto Solera-Rico, Carlos Sanmiguel Vila, M. A. Gómez, Yuning Wang

Variational autoencoder (VAE) architectures have the potential to develop reduced-order models (ROMs) for chaotic fluid flows. We propose a method for learning compact and near-orthogonal ROMs using a combination of a $\beta$-VAE and a transformer, tested on numerical data from a two-dimensional viscous flow in both periodic and chaotic regimes. The $\beta$-

Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou

The ability to efficiently plan and execute automated and precise search missions using unmanned aerial vehicles (UAVs) during emergency response situations is imperative. Precise navigation between obstacles and time-efficient searching of 3D structures and buildings are essential for locating survivors and people in need in emergency response missions. In

J. M. Gomez Llorente, J. Plata

We analytically study the dynamics of a solitonic vortex (SV) in a superfluid confined in a non-axisymmetric harmonic trap. The study provides a framework for analyzing the role of the trap anisotropy in the oscillation of SVs observed in recent experiments on atomic Bose and Fermi superfluids. The emergence of common and statistics-dependent features is tra

Lei Ma, Tian-Yi Wang

This paper is concerned with the convergence rates of subsonic flows for airfoil problem and infinite long largely-open nozzle problem, which is an improvement of [7,11,15,20]. The maximum principle is applied to estimate the potential function, by choosing the proper compared functions. Then, by the weighted Schauder estimates, the convergence rates of velo

Stéphane Bessy, Stéphan Thomassé, Laurent Viennot

In a directed graph $D$ on vertex set $v_1,\dots ,v_n$, a \emph{forward arc} is an arc $v_iv_j$ where $i<j$. A pair $v_i,v_j$ is \emph{forward connected} if there is a directed path from $v_i$ to $v_j$ consisting of forward arcs. In the {\tt Forward Connected Pairs Problem} ({\tt FCPP}), the input is a strongly connected digraph $D$, and the output is the ma

Ryoji Tanabe

Although the population size is an important parameter in evolutionary multi-objective optimization (EMO), little is known about its influence on preference-based EMO (PBEMO). The effectiveness of an unbounded external archive (UA) in PBEMO is also poorly understood, where the UA maintains all non-dominated solutions found so far. In addition, existing metho

Maximilian Nitsch, David Stenger, Dirk Abel

The performance of navigation algorithms significantly determines the trajectory tracking accuracy of the guidance, navigation, and control (GNC) system of an autonomous underwater vehicle (AUV). In closed-loop operation, the interaction among path planning, control, and navigation plays a crucial role in the tracking accuracy of the overall GNC system. A Do

Sk. Aziz, Arindam Ghosh, Om Prakash

In this paper, we introduce a new class of derivations that generalizes skew derivations and semi-derivations, and we call it ``skew semi-derivation". Further, we present a study of the conditions under which this type of multiplicative derivation becomes additive.

Saikat Mondal, Mohammad Masudur Rahman, Chanchal K. Roy

In Stack Overflow (SO), the quality of posts (i.e., questions and answers) is subjectively evaluated by users through a voting mechanism. The net votes (upvotes - downvotes) obtained by a post are often considered an approximation of its quality. However, about half of the questions that received working solutions got more downvotes than upvotes. Furthermore

Xoel Sixto, Guillermo Currás-Lorenzo, Kiyoshi Tamaki, Marcos Curty

Decoy-state quantum key distribution (QKD) is undoubtedly the most efficient solution to handle multi-photon signals emitted by laser sources, and provides the same secret key rate scaling as ideal single-photon sources. It requires, however, that the phase of each emitted pulse is uniformly random. This might be difficult to guarantee in practice, due to in

Praveen Sai Bere, Mohammed Zafar Ali Khan, Lajos Hanzo

V2X (Vehicle-to-everything) communication relies on short messages for short-range transmissions over a fading wireless channel, yet requires high reliability and low latency. Hard-decision decoding sacrifices the preservation of diversity order, leading to pronounced performance degradation in fading channels. By contrast, soft-decision decoding retains div

Antyanta Bangunharcana, Ahmed Magd, Kyung-Soo Kim

Self-supervised multi-frame depth estimation achieves high accuracy by computing matching costs of pixel correspondences between adjacent frames, injecting geometric information into the network. These pixel-correspondence candidates are computed based on the relative pose estimates between the frames. Accurate pose predictions are essential for precise matc

Grzegorz Czelusta, Jakub Mielczarek

Spin network states are a powerful tool for constructing the $SU(2)$ gauge theories on a graph. In loop quantum gravity (LQG), they have yielded many promising predictions, although progress has been limited by the computational challenge of dealing with high-dimensional Hilbert spaces. To explore more general configurations, quantum computing methods can be

Yalu Wang, Zhijie Han, Jie Li, Xin He

With the development of the Internet of Things (IoT), network intrusion detection is becoming more complex and extensive. It is essential to investigate an intelligent, automated, and robust network intrusion detection method. Graph neural networks based network intrusion detection methods have been proposed. However, it still needs further studies because t

Georgy V. Pushkarev, Vladimir G. Mazurenko, Vladimir V. Mazurenko, Danil W. Boukhvalov

The role of interlayer bonds in the two-dimensional (2D) materials "beyond graphene" and so-called van der Waals heterostructures is vital, and understanding the nature of these bonds in terms of strength and type is essential due to a wide range of their prospective technological applications. However, this issue has not yet been properly addressed in the p

Alexander Rogozin, Anton Novitskii, Alexander Gasnikov

Decentralized optimization is well studied for smooth unconstrained problems. However, constrained problems or problems with composite terms are an open direction for research. We study structured (or composite) optimization problems, where the functional is a sum of a convex smooth function and a proper convex proximal-friendly term. Our method builds upon

Lei Ma, Jingyang Zhang, Ke Deng, Peng Xue

Dental template and parametric dental models are important tools for various applications in digital dentistry. However, constructing an unbiased dental template and accurate parametric dental models remains a challenging task due to the complex anatomical and morphological dental structures and also low volume ratio of the teeth. In this study, we develop a

Aida Abiad, Francesco Belardo, Antonina P. Khramova

A gain graph over a group $G$, also referred to as $G$-gain graph, is a graph where an element of a group $G$, called gain, is assigned to each oriented edge, in such a way that the inverse element is associated with the opposite orientation. Gain graphs can be regarded as a generalization of signed graphs, among others. In this work, we show a new switching

Yutaro Kado, Shin-ichi Sasa

The steady propagation of a $(d-1)$-dimensional planer interface in $d$-dimensional space is studied by analyzing mesoscopic non-conserved order parameter dynamics with two local minima under the influence of thermal noise. In this analysis, an entropic force generating interface propagation is formulated using a perturbation method. It is found that the ent

Alexander Stangl, Dolors Pla, Caroline Pirovano, Odette Chaix-Pluchery

We have developed a novel in situ methodology for the direct study of mass transport properties in oxides with spatial and unprecedented time resolution, based on Raman spectroscopy coupled to isothermal isotope exchanges. Changes in the isotope concentration, resulting in a Raman frequency shift, can be followed in real time, not accessible by conventional

Hamid El Bahja, Jan Christian Hauffen, Peter Jung, Bubacarr Bah

Deep learning has been highly successful in some applications. Nevertheless, its use for solving partial differential equations (PDEs) has only been of recent interest with current state-of-the-art machine learning libraries, e.g., TensorFlow or PyTorch. Physics-informed neural networks (PINNs) are an attractive tool for solving partial differential equation

Pan Tan, Mingchen Li, Liang Zhang, Zhiqiang Hu

We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling. By assembling an extensive dataset of 96 million sequence-host bacterial strain optimal growth temperatures (OGTs) and {\Delta}Tm data for point mutations under consistent experimental conditions, we

Hugo Quard, Mario Khoury, Andong Wang, Tobias Herzig

The creation of fluorescent defects in silicon is a key stepping stone towards assuring the integration perspectives of quantum photonic devices into existing technologies. Here we demonstrate the creation, by femtosecond laser annealing, of W and G-centers in commercial silicon on insulator (SOI) previously implanted with 12C+ ions. Their quality is compara

Weijie Li, Wei Yang, Wenpeng Zhang, Tianpeng Liu

Vehicle recognition is a fundamental problem in SAR image interpretation. However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations. Additionally, the lack of large datasets further complicates the task. Inspired by the analysis of target signature variations and deep lea

Shahid Rabbani, Dimitris M. Manias, Dimitrios C. Kyritsis, Dimitris A. Goussis

Five recently developed chemical kinetics mechanisms for ammonia oxidation are analysed and compared, in the context of homogeneous adiabatic autoignition. The analysis focuses on the ignition delay and is based on the explosive mode that is shown to drive the process. Using algorithmic tools based on the Computational Singular Perturbation algorithm, the re

Guangsheng Bao, Zebin Ou, Yue Zhang

Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewri

M. Ishchenko, M. Sobolenko, P. Berczik, S. Khoperskov

Context. Recent observational data show that the Milky Way (MW) galaxy contains about 170 globular clusters (GCs). A fraction of them is likely formed in dwarf galaxies accreted onto the MW in the past, while the remaining of clusters are formed in-situ. Therefore, different parameters, including orbits, of the globular clusters is a valuable tool for studyi

Nicole Spillane

This work considers the convergence of GMRES for non-singular problems. GMRES is interpreted as the GCR method which allows for simple proofs of the convergence estimates. Preconditioning and weighted norms within GMRES are considered. The objective is to provide a way of choosing the preconditioner and GMRES norm that ensure fast convergence. The main focus

Alessandro Achille, Michael Kearns, Carson Klingenberg, Stefano Soatto

Responsible use of data is an indispensable part of any machine learning (ML) implementation. ML developers must carefully collect and curate their datasets, and document their provenance. They must also make sure to respect intellectual property rights, preserve individual privacy, and use data in an ethical way. Over the past few years, ML models have sign

Gahee Kim, Minwoo Jeon, Hyun Do Choi, Jun Ki Cho

In this study, a novel active solubility sensing device using computer vision is proposed to improve separation purification performance and prevent malfunctions of separation equipment such as preparative liquid chromatographers and evaporators. The proposed device actively measures the solubility by transmitting a solution using a background image. The pro

Xiaobao Wu, Xinshuai Dong, Thong Nguyen, Chaoqun Liu

Cross-lingual topic models have been prevalent for cross-lingual text analysis by revealing aligned latent topics. However, most existing methods suffer from producing repetitive topics that hinder further analysis and performance decline caused by low-coverage dictionaries. In this paper, we propose the Cross-lingual Topic Modeling with Mutual Information (

Witold Wydmański, Oleksii Bulenok, Marek Śmieja

Deep learning has achieved impressive performance in many domains, such as computer vision and natural language processing, but its advantage over classical shallow methods on tabular datasets remains questionable. It is especially challenging to surpass the performance of tree-like ensembles, such as XGBoost or Random Forests, on small-sized datasets (less

Xuhai Chen, Jiangning Zhang, Chao Xu, Yabiao Wang

Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-variant due to object motion, out-of-focus, etc., resulting in severe performance drop of the advanced SR methods. To address this problem, we firstly introduce two new datasets wit

Wen-Xiang Chen, Yao-Guang Zheng

This article suggests that Bose-Einstein condensation can occur without the need for an energy barrier when the boundary conditions are set as $\frac{T}{T_{c}}=z$ (where z is a positive integer). Under these conditions, bosons can condense in the Schwarzschild black hole. The incident waves can then be trapped and condensed within the black hole, generating

Thomas Debris-Alazard

These lecture notes have been written for courses given at \'Ecole normale sup\'erieure de Lyon and summer school 2022 in post-quantum cryptography that took place in the university of Budapest. Our objective is to give a general introduction to the foundations of code-based cryptography which is currently known to be secure even against quantum adversaries.

Sibei Chen, Hanbing Liu, Weiting Jin, Xiangyu Sun

Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming. Despite the impressive capabilities of large language models like ChatGPT in generating programs by interacting with users through natural language prompts, there are still limitations. Specifically, a user

Mohammad Ali Jamshidi, Hadi Veisi, Mohammad Mahdi Mojahedian, Mohammad Reza Aref

Inference centers need more data to have a more comprehensive and beneficial learning model, and for this purpose, they need to collect data from data providers. On the other hand, data providers are cautious about delivering their datasets to inference centers in terms of privacy considerations. In this paper, by modifying the structure of the autoencoder,

Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta, Hiroyuki Abe

Pathological image analysis is an important process for detecting abnormalities such as cancer from cell images. However, since the image size is generally very large, the cost of providing detailed annotations is high, which makes it difficult to apply machine learning techniques. One way to improve the performance of identifying abnormalities while keeping

Hazrat Ali, Christer Gronlund, Zubair Shah

Artificial Intelligence (AI)-based models can help in diagnosing COVID-19 from lung CT scans and X-ray images; however, these models require large amounts of data for training and validation. Many researchers studied Generative Adversarial Networks (GANs) for producing synthetic lung CT scans and X-Ray images to improve the performance of AI-based models. It

Utsav Singh, Vinay P. Namboodiri

Hierarchical reinforcement learning (HRL) leverages temporal abstraction to efficiently tackle complex long-horizon tasks. However, HRL often collapses because the continual updates of the low-level primitive make earlier sub-goals issued by the high-level policy obsolete, introducing non-stationarity that destabilizes training. We propose CRISP, a curriculu

Xin Liu, Di Luo, Zhenrong Zhang, Kejin Wei

Mode-pairing quantum key distribution (MP-QKD) is an easy-to-implement scheme that transcends the Pirandola--Laurenza--Ottaviani--Banchi bound without using quantum repeaters. In this paper, we present an improvement of the performance of MP-QKD using an advantage distillation method. The simulation results demonstrate that the proposed scheme extends the tr

Sajad Salavatidezfouli, Arash Hajisharifi, Michele Girfoglio, Giovanni Stabile

Tumble dryers offer a fast and convenient way of drying textiles independent of weather conditions and therefore are frequently used in ordinary households. However, artificial drying of textiles consumes considerable amounts of energy, approximately 8.2 percent of the residential electricity consumption is for drying of textiles in northern European countri

Xinshun Wang, Qiongjie Cui, Chen Chen, Shen Zhao

In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory. Recently, MLP-Mixer, initially developed for vision tasks, has been leveraged into human motion prediction as a promising alternative to GCNs, which achieves both better performance and better efficiency than GC

Shulin Huang, Shirong Ma, Yangning Li, Yinghui Li

Entity Set Expansion (ESE) is a critical task aiming at expanding entities of the target semantic class described by seed entities. Most existing ESE methods are retrieval-based frameworks that need to extract contextual features of entities and calculate the similarity between seed entities and candidate entities. To achieve the two purposes, they iterative

Kun Xu, Hao-Ran Yang, Ying-Han Mao, Xiao-Tian Xu

Neutron stars (NSs) play essential roles in modern astrophysics. Magnetic fields and spin periods of newborn (zero age) NSs have large impact on the further evolution of NSs, which are however poorly explored in observation due to the difficulty of finding newborn NSs. In this work, we aim to infer the magnetic fields and spin periods (Bi and Pi) of zero-age

Rong Dong, Dongsheng Li

Interior pointwise $C^{1,\alpha}$ estimates are established for Stokes systems in divergence form where no continuity in time variable is assumed for the coefficients and the given data. The estimates are attained by iteration and are presented by Campanato's characterization. The sharpness of the conclusions can be seen from Serrin's counterexample.

Liang Sun, Yuanbo Li, Mohan Chen

Adopting an accurate kinetic energy density functional (KEDF) to characterize the noninteracting kinetic energy within the framework of orbital-free density functional theory (OFDFT) is challenging. We propose a new form of the non-local KEDF with a real-space truncation cutoff that satisfies the uniform electron gas limit and design KEDFs for simple metals

Matthias Kluge, Ralf Bender

We investigate the roles of major and minor mergers during brightest cluster galaxy (BCG) assembly using surface brightness profiles, line indices, and fundamental plane relations. Based on our own sample and consistently reanalyzed Sloan Digital Sky Survey data, we find that BCGs and luminous normal ellipticals (LNEs) have similar central velocity dispersio

Leheng Li, Qing Lian, Luozhou Wang, Ningning Ma

This work explores the use of 3D generative models to synthesize training data for 3D vision tasks. The key requirements of the generative models are that the generated data should be photorealistic to match the real-world scenarios, and the corresponding 3D attributes should be aligned with given sampling labels. However, we find that the recent NeRF-based

Mohib Jafri, Andy Wu

Using a combination of incentive modeling and empirical meta-analyses, this paper provides a pointed critique at the incentive systems that drive venture capital firms to optimize their practices towards activities that increase General Partner utility yet are disjoint from improving the underlying asset of startup equity. We propose a "distributed venture f

Abhijeet Alase, Emilio Cobanera, Gerardo Ortiz, Lorenza Viola

Both the physics and applications of fermionic symmetry-protected topological phases rely heavily on a principle known as bulk-boundary correspondence, which predicts the emergence of protected boundary-localized energy excitations (boundary states) if the bulk is topologically non-trivial. Current theoretical approaches formulate a bulk-boundary corresponde

Juan Serratos

In 1966, David Mumford created a drawing of $\operatorname{Proj} \mathbb Z [X,Y]$ in his book, "Lectures on Curves on an Algebraic Surface". In following, he created a photo of a so-called 'arithmetic surface' $\operatorname{Spec} \mathbb Z [T]$for his 1988 book, "The Red Book of Varieties and Schemes". The depiction presents the structure of $\operatorname{

Wonjun Yi, Jung-Woo Choi

In-situ classification of faulty sounds is an important issue in machine health monitoring and diagnosis. However, in a noisy environment such as a factory, machine sound is always mixed up with environmental noises, and noise-only periods can exist when a machine is not in operation. Therefore, a deep neural network (DNN)-based fault classifier has to be ab

M. Kazem Negahdari, Abdollah Langari

We investigate the nonlinear response of the Kitaev honeycomb lattice model in a weak magnetic field using the theory of two-dimensional coherent spectroscopy. We observe that at the isotropic point in the non-Abelian phase of this model, the nonlinear spectrum in the 2D frequency domain consists of sharp signals that originate from the flux excitations and

Ludovico Scarton, Alexander Hagg

The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance. Examination of the suitability of widely used representations for quality diversity optimization (QD) in robotic domains has yielded inconsistent results regarding the most appropriate encoding method. Given the domain-dependent nature of QD, a

Robin Strässer, Julian Berberich, Frank Allgöwer

Data-driven analysis and control of dynamical systems have gained a lot of interest in recent years. While the class of linear systems is well studied, theoretical results for nonlinear systems are still rare. In this paper, we present a data-driven controller design method for discrete-time control-affine nonlinear systems. Our approach relies on the Koopma

Sriya Rallabandi, Sanchit Singhal, Pratinav Seth

This paper describes our submission to Task 10 at SemEval 2023-Explainable Detection of Online Sexism (EDOS), divided into three subtasks. The recent rise in social media platforms has seen an increase in disproportionate levels of sexism experienced by women on social media platforms. This has made detecting and explaining online sexist content more importa

Levent Kargın, Ayhan Dil, Mehmet Cenkci, Mümün Can

The aim of this paper is to investigate harmonic Stieltjes constants occurring in the Laurent expansions of the function \[ \zeta_{H}\left( s,a\right) =\sum_{n=0}^{\infty}\frac{1}{\left( n+a\right) ^{s}}\sum_{k=0}^{n}\frac{1}{k+a},\text{ }\operatorname{Re}\left( s\right) >1, \] which we call harmonic Hurwitz zeta function. In particular evaluation formulas f

Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He

Recommender systems typically retrieve items from an item corpus for personalized recommendations. However, such a retrieval-based recommender paradigm faces two limitations: 1) the human-generated items in the corpus might fail to satisfy the users' diverse information needs, and 2) users usually adjust the recommendations via inefficient passive feedback,

Jenthe Thienpondt, Nilesh Madhu, Kris Demuynck

This paper is concerned with the task of speaker verification on audio with multiple overlapping speakers. Most speaker verification systems are designed with the assumption of a single speaker being present in a given audio segment. However, in a real-world setting this assumption does not always hold. In this paper, we demonstrate that current speaker veri

Yuze Wu, Fan Yang, Ze Wang, Kaiwei Wang

This letter presents a novel and retractable ring-shaped quadrotor called Ring-Rotor that can adjust the vehicle's length and width simultaneously. Unlike other morphing quadrotors with high platform complexity and poor controllability, Ring-Rotor uses only one servo motor for morphing but reduces the largest dimension of the vehicle by approximately 31.4\%.

Gyula Lakos

We review and provide simplified proofs related to the Magnus expansion, and improve convergence estimates. Observations and improvements concerning the Baker--Campbell--Hausdorff expansion are also made. In this Part IIA, we investigate the case of $2\times2$ matrices with respect to the operator norm. We consider norm estimates and minimal presentations in

Kun Zhu, Xiaocheng Feng, Xiachong Feng, Yingsheng Wu

Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy. We observe that a high-quality catalogue-guided generation process can effectively alleviate this problem. Therefore, we present an atomic an

Shree. Dolax Ray, Mst. Khadija Tul Kubra Natasha, Md. Azizul Hakim, Fatema Nur

Carrot is a famous nutritional vegetable and developed all over the world. Different diseases of Carrot has become a massive issue in the carrot production circle which leads to a tremendous effect on the economic growth in the agricultural sector. An automatic carrot disease detection system can help to identify malicious carrots and can provide a guide to

Antonio Purificato, Giulia Cassarà, Federico Siciliano, Pietro Liò

Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships.

Raghavendra Ramachandra, Sushma Venkatesh, Naser Damer, Narayan Vetrekar

Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack

Amena Begum Farha, Md. Azizul Hakim, Mst. Eshita Khatun

Flower breed detection and giving details of that breed with the suggestion of cultivation processes and the way of taking care is important for flower cultivation, breed invention, and the flower business. Among all the local flowers in Bangladesh, the rose is one of the most popular and demanded flowers. Roses are the most desirable flower not only in Bang

JuAe Song

We prove that an injective $\boldsymbol{T}$-algebra homomorphism between the rational function semifields of two tropical curves induces a surjective morphism between those tropical curves, where $\boldsymbol{T}$ is the tropical semifield $(\boldsymbol{R} \cup \{ -\infty \}, \operatorname{max}, +)$.

Feng Ji, See Hian Lee, Kai Zhao, Wee Peng Tay

In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP). While it is common in GSP to impose signal smoothness constraints in learning and estimation tasks, it is unclear how this can be done for discrete node labels. We bridge this gap by introducing the concept of distribut

Zhendong Chen

In this paper, we consider the shock formation problem for the 3-dimensional(3D) compressible Euler equations with damping inspired by the work \cite{BSV3Dfulleuler}. It will be shown that for a class of large data, the damping can not prevent the formation of point shock, and the damping effect shifts the shock time and the wave amplitude while the shock lo

Yueheng Li, Hao Chen, Bowei Xu, Zicheng Zhang

Effective Adaptive BitRate (ABR) algorithm or policy is of paramount importance for Real-Time Video Communication (RTVC) amid this pandemic to pursue uncompromised quality of experience (QoE). Existing ABR methods mainly separate the network bandwidth estimation and video encoder control, and fine-tune video bitrate towards estimated bandwidth, assuming the

Irinel Caprini

Using a full resummation of the Adler function in the large-$\beta_0$ approximation of QCD and a mathematical framework of resurgence suitable for the specific properties of the Borel transform in this particular case, we derive a compact resurgent representation of the QCD Adler function, valid in the whole complex momentum plane. The representation is expr

Christian Blohmann, Stefano Ronchi, Alan Weinstein

We extend to Poisson manifolds the theory of hamiltonian Lie algebroids originally developed by two of the authors for presymplectic manifolds. As in the presymplectic case, our definition, involving a vector bundle connection on the Lie algebroid, reduces to the definition of hamiltonian action for an action Lie algebroid with the trivial connection. The cl

Jaeho Jeong, Hosung Park, Hee-Youl Kwak, Jong-Seon No

Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decodin

Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-valued embedding vectors for pairwise similarity computation, and all embeddings are traditionally restricted to a uniform size that is relatively large (e.g., 256-dimensional). With

Éric Fogret, Pierre Pellat-Finet

A light ray in space is characterized by two vectors: (i) a transverse spatial-vector associated with the point where the ray intersects a given spherical cap; (ii) an angular-frequency vector which defines the ray direction of propagation. Given a light ray propagating from a spherical emitter to a spherical receiver, a linear equation is established that l

Nandita Barman, Anjali Aleria, Premananda Bera

This paper carries out a linear stability analysis of a plane Couette flow in a porous layer underlying a fluid layer where the porous layer is anisotropic and inhomogeneous. The plane Couette flow is induced due to the uniform movement of the upper plate and convection arises due to the maintenance of the temperature difference between the upper plate and t

Vaibhav Tiwari

The black hole masses measured from gravitational wave observations appear to cluster around specific mass values. Consequently, the primary~(and chirp) mass distribution of binary black holes inferred using these measurements shows four emerging peaks. These peaks are approximately located at a primary~(chirp) mass value of 10$M_\odot$~(8$M_\odot$), 20$M_\o

Sang-Bin Jeon, Jaeho Jung, Jinhyung Park, In-Kwon Lee

In order to serve better VR experiences to users, existing predictive methods of Redirected Walking (RDW) exploit future information to reduce the number of reset occurrences. However, such methods often impose a precondition during deployment, either in the virtual environment's layout or the user's walking direction, which constrains its universal applicat

Zhe Tao, Stephanie Nawas, Jacqueline Mitchell, Aditya V. Thakur

Deep neural networks (DNNs) are becoming increasingly important components of software, and are considered the state-of-the-art solution for a number of problems, such as image recognition. However, DNNs are far from infallible, and incorrect behavior of DNNs can have disastrous real-world consequences. This paper addresses the problem of architecture-preser

Deunsol Jung, Sanghyun Kim, Won Hwa Kim, Minsu Cho

Scene graph generation aims to construct a semantic graph structure from an image such that its nodes and edges respectively represent objects and their relationships. One of the major challenges for the task lies in the presence of distracting objects and relationships in images; contextual reasoning is strongly distracted by irrelevant objects or backgroun

Brian McReynolds, Rui Graca, Tobi Delbruck

Dynamic Vision Sensors (DVS) record "events" corresponding to pixel-level brightness changes, resulting in data-efficient representation of a dynamic visual scene. As DVS expand into increasingly diverse applications, non-ideal behaviors in their output under extreme sensing conditions are important to consider. Under low illumination (below ~10 lux) their o

Yiwen Ye, Yutong Xie, Jianpeng Zhang, Ziyang Chen

The universal model emerges as a promising trend for medical image segmentation, paving up the way to build medical imaging large model (MILM). One popular strategy to build universal models is to encode each task as a one-hot vector and generate dynamic convolutional layers at the end of the decoder to extract the interested target. Although successful, it

Dohae Lee, Hyun Kang, In-Kwon Lee

We present ClothCombo, a pipeline to drape arbitrary combinations of clothes on 3D human models with varying body shapes and poses. While existing learning-based approaches for draping clothes have shown promising results, multi-layered clothing remains challenging as it is non-trivial to model inter-cloth interaction. To this end, our method utilizes a GNN-

Yu-Ying He, Xin-Jian Wen

The thermodynamics of finite size quark matter in the quasiparticle model is self-consistently constructed by an effective bag function, which presents the medium effect to the confinement. We obtained completely analytic surface tension in the strong magnetic field with the multiple reflection expansion. The anisotropic structure is demonstrated by the spli

Sonja Cox, Christa Cuchiero, Asma Khedher

We introduce and analyse infinite dimensional Wishart processes taking values in the cone $S^+_1(H)$ of positive self-adjoint trace class operators on a separable real Hilbert space $H$. Our main result gives necessary and sufficient conditions for their existence, showing that these processes are necessarily of fixed finite rank almost surely, but they are

Jingjie Ni, Fangfei Li, Zheng-Guang Wu

In this paper, a deep reinforcement learning based method is proposed to obtain optimal policies for optimal infinite-horizon control of probabilistic Boolean control networks (PBCNs). Compared with the existing literatures, the proposed method is model-free, namely, the system model and the initial states needn't to be known. Meanwhile, it is suitable for l

E. I. Kaptsov, V. A. Dorodnitsyn

Invariant finite-difference schemes for the one-dimensional shallow water equations in the presence of a magnetic field for various bottom topographies are constructed. Based on the results of the group classification recently carried out by the authors, finite-difference analogues of the conservation laws of the original differential model are obtained. Som

Ali TehraniJamsaz, Alok Mishra, Akash Dutta, Abid M. Malik

GPU-based HPC clusters are attracting more scientific application developers due to their extensive parallelism and energy efficiency. In order to achieve portability among a variety of multi/many core architectures, a popular choice for an application developer is to utilize directive-based parallel programming models, such as OpenMP. However, even with Ope

Subin Sahayam, John Zakkam, Umarani Jayaraman

In deep learning, mini-batch training is commonly used to optimize network parameters. However, the traditional mini-batch method may not learn the under-represented samples and complex patterns in the data, leading to a longer time for generalization. To address this problem, a variant of the traditional algorithm has been proposed, which trains the network

Petr M. Akhmet'ev

Desuspension Theorem for the steam $\Pi_{2^l-2}$ in stable homotopy groups of spheres is formulated and is proved. The proof is a minor revision of a theorem in the preprint \cite{A1} by the author (2010).

Adrian Dumitrescu, Csaba D. Tóth

For a polygon $P$ with holes in the plane, we denote by $\varrho(P)$ the ratio between the geodesic and the Euclidean diameters of $P$. It is shown that over all convex polygons with $h$~convex holes, the supremum of $\varrho(P)$ is between $\Omega(h^{1/3})$ and $O(h^{1/2})$. The upper bound improves to $\varrho(P)\leq O(1+\min\{h^{3/4}\Delta,h^{1/2}\Delta^{

Seongmin Hong, Se Young Chun

Normalizing flows have been successfully modeling a complex probability distribution as an invertible transformation of a simple base distribution. However, there are often applications that require more than invertibility. For instance, the computation of energies and forces in physics requires the second derivatives of the transformation to be well-defined

Berk Iskender, Marc L. Klasky, Yoram Bresler

Dynamic imaging addresses the recovery of a time-varying 2D or 3D object at each time instant using its undersampled measurements. In particular, in the case of dynamic tomography, only a single projection at a single view angle may be available at a time, making the problem severely ill-posed. We propose an approach, RED-PSM, which combines for the first ti

Silu Guo, Hwanhui Yun, Sreejith Nair, Bharat Jalan

Restructuring of rutile TiO$_{2}$ under electron beam irradiation driven by radiolysis was observed and analyzed using a combination of atomic-resolution imaging and electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). It was determined that a high-energy (80-300 keV) electron beam at high doses ($\gtrapprox 10^7 \ e/

Gaojie Wu, Wei-Shi Zheng, Yutong Lu, Qi Tian

Vision Transformer (ViT) has shown great potential for various visual tasks due to its ability to model long-range dependency. However, ViT requires a large amount of computing resource to compute the global self-attention. In this work, we propose a ladder self-attention block with multiple branches and a progressive shift mechanism to develop a light-weigh

G. M. Vanacore, L. F. Zagonel, N. Barrett

The influence of surface enhanced covalency on the Madelung potential is experimentally investigated using angle-resolved photoemission for (100), (110) and (111) SrTiO$_3$ surfaces after annealing in UHV at 630 {\deg}C. Deconvolution of the core level spectra (O 1s, Sr 3d and Ti 2p) distinguishes bulk and surface components, which are interpreted in terms o

Haochen Pi, Keith Burghardt, Allon G. Percus, Kristina Lerman

Real-world networks are rarely static. Recently, there has been increasing interest in both network growth and network densification, in which the number of edges scales superlinearly with the number of nodes. Less studied but equally important, however, are scaling laws of higher-order cliques, which can drive clustering and network redundancy. In this pape

Liang Xu

We follow the method of ABP estimate in \cite{brendle2021} and apply it to spacelike submanifolds in $\mathbb R^{n,1}$. We then obtain Michael-Simon type inequalities. Surprisingly, our investigation leads to a Sobolev inequality without a mean curvature term, provided the hypersurface is mean convex.